Due to lack of experimental data and sophisticated models derived from such data, most data processing algorithms from the sensor network literature are evaluated with data generated from simple parametric models. Unfortunately, the type of data input used in the evaluation often significantly affect the algorithm performance. Our case studies of a few widely-studied sensor networks data processing algorithms demonstrated the need to evaluate algorithms with data across a range of parameters. In the end, we propose our synthetic data generation framework.